Jul 2026· ESMO real world data and digital oncology· Vol 13, pp. 100731· 0 citations· 19 references
Medicine
TL;DR
An international ring test documenting CDSS usage, performance, and manual interpretation revealed clinically relevant discrepancies between laboratories and interpreters, underscoring the need for structured external quality assessment schemes for CDSS tools in addition to the existing laboratory workflow schemes.
Abstract
Background The EU4Health project PCM4EU aimed to improve survival rates and quality of life of patients with cancer based on precision cancer medicine. To achieve this, enhanced expertise and quality of molecular cancer diagnostics are key. Clinical decision support systems (CDSS) have become increasingly important after the introduction of comprehensive genomic diagnostic profiling. While external quality assessment schemes are mandatory for most diagnostic laboratory tests, similar programs for CDSS tools are currently lacking. To address this, we piloted an international ring test documenting CDSS usage, performance, and manual interpretation. Materials and methods Twenty synthetic datasets were generated, mimicking small variant call sets from a typical targeted 500-gene panel (VCF format) across multiple cancer types (10 tumour-normal pairs and 10 tumour-only). Participants received standardised instructions via e-mail and at a virtual meeting and submitted results using a structured response form. Results Eight laboratories from seven countries participated. All participants submitted results for the 10 tumour-only cases; one submitted results from two assessors. Tumour-only cases contained 6-18 variants where interpretation could be critical. Oncogenic calls for hotspot variants showed good agreement across the various CDSS tools applied; however, variability existed regarding reported variants and clinical interpretation. Conclusion The pilot ring test revealed clinically relevant discrepancies between laboratories and interpreters, underscoring the need for structured external quality assessment schemes for CDSS tools in addition to the existing laboratory workflow schemes. It also highlighted several challenges related to the generation of realistic synthetic data, the design of reporting formats, the definition of ground truth, and the manual interpretation of results.
Novel artificial intelligence-based clinical decision support (CDS) technology is assessed to develop and validate standard of care (SoC) to streamline the breast MDT meetings in a tertiary cancer centre to deliver a sustainable clinical decision pathway.
H. Jeffery, A. Sinha, B. Shifa et al.· BMJ Health & Care Informatic...· 0 citations
An electronic health record-based prognostic model is developed to identify patients with metastatic breast cancer at high risk of near-term death and can predict risk of death within 30 and 90 days for patients with MBC.
E. Ray, Xin-Yi Zhang, Lisette N. Dunham et al.· JCO Oncology Practice· 0 citations
Abstract Background Maintenance of oncology clinical practice guidelines (CPGs) is increasingly challenged by the rapid growth of trial data and therapeutic complexity. While large language models (LLMs) have shown promise in information retrieval, their utility in the rigorous, end-to-end workflow of guideline mainten...
Manuel Knauer, Julian Greß, J. Kather et al.· JMIR AI· 0 citations
Although the evaluated LLMs did not outperform traditional supervised models, the study provides a clear performance baseline for future research on structured clinical prediction with language models and shows that LLMs may offer value as complementary exploratory tools, but their outputs should be interpreted only wi...
Habibe Karayiğit, F. Kalelioğlu· International Journal of Int...· 0 citations
The appropriate role of AI-assisted decision support as an adjunct to, rather than a substitute for, multidisciplinary review in rectal cancer management is defined.
Ryan J Meyer, Tamir E. Bresler, Tadevos T Makaryan et al.· The American surgeon· 0 citations
Current evidence is insufficient to establish improvements in MDT decision quality or patient outcomes, and AI should be regarded as a supervised support tool rather than a replacement for expert multidisciplinary judgment.
A. Nikitaras, S. M. Tsoti, M. Pramateftakis· Frontiers in Oncology· 0 citations
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